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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "kernelspec": {
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    "interpreter": {
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 "cells": [
  {
   "source": [
    "# SVM中使用多项式特征"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "X ,y = datasets.make_moons()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "X ,y = datasets.make_moons(noise=0.15, random_state=666)\n",
    "\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 使用多项式特征的SVM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "def PolynomialSVC(degree, C=1.0):\n",
    "    return Pipeline([\n",
    "        (\"poly\", PolynomialFeatures(degree=degree)),\n",
    "        (\"std_scaler\", StandardScaler()),\n",
    "        (\"linearSVC\", LinearSVC(C=C))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_svc = PolynomialSVC(degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('poly',\n",
       "                 PolynomialFeatures(degree=3, include_bias=True,\n",
       "                                    interaction_only=False, order='C')),\n",
       "                ('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('linearSVC',\n",
       "                 LinearSVC(C=1.0, class_weight=None, dual=True,\n",
       "                           fit_intercept=True, intercept_scaling=1,\n",
       "                           loss='squared_hinge', max_iter=1000,\n",
       "                           multi_class='ovr', penalty='l2', random_state=None,\n",
       "                           tol=0.0001, verbose=0))],\n",
       "         verbose=False)"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "source": [
    "poly_svc.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "\n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1,1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1,1)\n",
    "        )\n",
    "\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "\n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "\n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A', '#FFF59D','#90CAF9'])\n",
    "\n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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296.924918 87.583508 \r\nL 296.337549 86.972231 \r\nL 296.337549 86.098978 \r\nL 296.337549 85.225725 \r\nL 296.337549 84.352472 \r\nL 296.08582 84.090496 \r\nL 295.498452 83.479219 \r\nL 295.498452 82.605966 \r\nL 295.498452 81.732713 \r\nL 295.246722 81.470737 \r\nL 294.659354 80.85946 \r\nL 294.659354 79.986207 \r\nL 294.659354 79.112954 \r\nL 294.407625 78.850978 \r\nL 293.820256 78.239701 \r\nL 293.820256 77.366448 \r\nL 293.820256 76.493195 \r\nL 293.568527 76.231219 \r\nL 292.981158 75.619942 \r\nL 292.981158 74.746689 \r\nL 292.981158 73.873436 \r\nL 292.981158 73.000183 \r\nL 292.729429 72.738207 \r\nL 292.142061 72.12693 \r\nL 292.142061 71.253677 \r\nL 292.142061 70.380424 \r\nL 291.890331 70.118448 \r\nL 291.302963 69.507171 \r\nL 291.302963 68.633918 \r\nL 291.302963 67.760665 \r\nL 291.051234 67.498689 \r\nL 290.463865 66.887412 \r\nL 290.463865 66.014159 \r\nL 290.463865 65.140905 \r\nL 290.463865 64.267652 \r\nL 290.212136 64.005677 \r\nL 289.624767 63.394399 \r\nL 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286.520106 36.323556 \r\nL 286.520106 35.450303 \r\nL 286.520106 34.57705 \r\nL 286.520106 33.703797 \r\nL 286.520106 32.830544 \r\nL 286.520106 31.957291 \r\nL 286.520106 31.084038 \r\nL 286.520106 30.210785 \r\nL 286.520106 29.337532 \r\nL 286.520106 28.464279 \r\nL 286.520106 27.591026 \r\nL 286.520106 26.717773 \r\nL 286.520106 25.84452 \r\nL 286.520106 24.971267 \r\nL 286.520106 24.098014 \r\nL 286.520106 23.224761 \r\nL 286.855745 22.87546 \r\nL 287.359203 22.351508 \r\nL 287.359203 21.478255 \r\nL 287.359203 20.605002 \r\nL 287.359203 19.731749 \r\nL 287.359203 18.858496 \r\nL 287.359203 17.985243 \r\nL 287.359203 17.11199 \r\nL 287.359203 16.238737 \r\nL 287.694843 15.889436 \r\nL 288.198301 15.365484 \r\nL 288.198301 14.492231 \r\nL 288.198301 13.618978 \r\nL 288.198301 12.745725 \r\nL 288.198301 11.872472 \r\nL 288.53394 11.523171 \r\nL 289.037399 10.999219 \r\nL 288.911534 10.999219 \r\nL 288.53394 11.392183 \r\nL 288.072437 11.872472 \r\nL 288.072437 12.745725 \r\nL 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236.50988 228.439219 \r\nL 237.348978 228.439219 \r\nL 238.188076 228.439219 \r\nL 239.027173 228.439219 \r\nL 239.866271 228.439219 \r\nL 240.705369 228.439219 \r\nL 241.544467 228.439219 \r\nL 242.383564 228.439219 \r\nL 243.222662 228.439219 \r\nL 244.06176 228.439219 \r\nL 244.900858 228.439219 \r\nL 245.739955 228.439219 \r\nL 246.579053 228.439219 \r\nL 247.418151 228.439219 \r\nL 248.257249 228.439219 \r\nL 249.096346 228.439219 \r\nL 249.935444 228.439219 \r\nL 250.774542 228.439219 \r\nL 251.61364 228.439219 \r\nL 252.452737 228.439219 \r\nL 253.291835 228.439219 \r\nL 254.130933 228.439219 \r\nL 254.970031 228.439219 \r\nL 255.809128 228.439219 \r\nL 256.648226 228.439219 \r\nL 257.487324 228.439219 \r\nL 258.326422 228.439219 \r\nL 259.165519 228.439219 \r\nL 260.004617 228.439219 \r\nL 260.843715 228.439219 \r\nL 261.682813 228.439219 \r\nL 262.52191 228.439219 \r\nL 263.361008 228.439219 \r\nL 264.200106 228.439219 \r\nL 265.039203 228.439219 \r\nL 265.878301 228.439219 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326.293339 228.439219 \r\nL 327.132437 228.439219 \r\nL 327.971534 228.439219 \r\nL 328.810632 228.439219 \r\nL 329.64973 228.439219 \r\nL 330.488828 228.439219 \r\nL 331.327925 228.439219 \r\nL 332.167023 228.439219 \r\nL 333.006121 228.439219 \r\nL 333.845219 228.439219 \r\nL 334.684316 228.439219 \r\nL 335.523414 228.439219 \r\nL 336.362512 228.439219 \r\nL 337.201609 228.439219 \r\nL 338.040707 228.439219 \r\nL 338.879805 228.439219 \r\nL 339.718903 228.439219 \r\nL 340.558 228.439219 \r\nL 341.397098 228.439219 \r\nL 342.236196 228.439219 \r\nL 343.075294 228.439219 \r\nL 343.914391 228.439219 \r\nL 344.753489 228.439219 \r\nL 345.592587 228.439219 \r\nL 346.431685 228.439219 \r\nL 347.270782 228.439219 \r\nL 348.10988 228.439219 \r\nL 348.948978 228.439219 \r\nL 349.788076 228.439219 \r\nL 350.627173 228.439219 \r\nL 351.466271 228.439219 \r\nL 352.305369 228.439219 \r\nL 353.144467 228.439219 \r\nL 353.983564 228.439219 \r\nL 354.822662 228.439219 \r\nL 355.66176 228.439219 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215.340424 \r\nL 373.282812 214.467171 \r\nL 373.282812 213.593918 \r\nL 373.282812 212.720665 \r\nL 373.282812 211.847412 \r\nL 373.282812 210.974159 \r\nL 373.282812 210.100905 \r\nL 373.282812 209.227652 \r\nL 373.282812 208.354399 \r\nL 373.282812 207.481146 \r\nL 373.282812 206.607893 \r\nL 373.282812 205.73464 \r\nL 373.282812 204.861387 \r\nL 373.282812 203.988134 \r\nL 373.282812 203.114881 \r\nL 373.282812 202.241628 \r\nL 373.282812 201.368375 \r\nL 373.282812 200.495122 \r\nL 373.282812 199.621869 \r\nL 373.282812 198.748616 \r\nL 373.282812 197.875363 \r\nL 373.282812 197.00211 \r\nL 373.282812 196.128857 \r\nL 373.282812 195.255604 \r\nL 373.282812 194.382351 \r\nL 373.282812 193.509098 \r\nL 373.282812 192.635845 \r\nL 373.282812 191.762592 \r\nL 373.282812 190.889339 \r\nL 373.282812 190.016086 \r\nL 373.282812 189.142833 \r\nL 373.282812 188.26958 \r\nL 373.282812 187.396327 \r\nL 373.282812 186.523074 \r\nL 373.282812 185.649821 \r\nL 373.282812 184.776568 \r\nL 373.282812 183.903315 \r\nL 373.282812 183.030062 \r\nL 373.282812 182.156809 \r\nL 373.282812 181.283556 \r\nL 373.282812 180.410303 \r\nL 373.282812 179.53705 \r\nL 373.282812 178.663797 \r\nL 373.282812 177.790544 \r\nL 373.282812 176.917291 \r\nL 373.282812 176.044038 \r\nL 373.282812 175.170785 \r\nL 373.282812 174.297532 \r\nL 373.282812 173.424279 \r\nL 373.282812 172.551026 \r\nL 373.282812 171.677773 \r\nL 373.282812 170.80452 \r\nL 373.282812 169.931267 \r\nL 373.282812 169.058014 \r\nL 373.282812 168.184761 \r\nL 373.282812 167.311508 \r\nL 373.282812 166.438255 \r\nL 373.282812 165.565002 \r\nL 373.282812 164.691749 \r\nL 373.282812 163.818496 \r\nL 373.282812 162.945243 \r\nL 373.282812 162.07199 \r\nL 373.282812 161.198737 \r\nL 373.282812 160.325484 \r\nL 373.282812 159.452231 \r\nL 373.282812 158.578978 \r\nL 373.282812 157.705725 \r\nL 373.282812 156.832472 \r\nL 373.282812 155.959219 \r\nL 373.282812 155.085966 \r\nL 373.282812 154.212713 \r\nL 373.282812 153.33946 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121.902351 \r\nL 373.282812 121.029098 \r\nL 373.282812 120.155845 \r\nL 373.282812 119.282592 \r\nL 373.282812 118.409339 \r\nL 373.282812 117.536086 \r\nL 373.282812 116.662833 \r\nL 373.282812 115.78958 \r\nL 373.282812 114.916327 \r\nL 373.282812 114.043074 \r\nL 373.282812 113.169821 \r\nL 373.282812 112.296568 \r\nL 373.282812 111.423315 \r\nL 373.282812 110.550062 \r\nL 373.282812 109.676809 \r\nL 373.282812 108.803556 \r\nL 373.282812 107.930303 \r\nL 373.282812 107.05705 \r\nL 373.282812 106.183797 \r\nL 373.282812 105.310544 \r\nL 373.282812 104.437291 \r\nL 373.282812 103.564038 \r\nL 373.282812 102.690785 \r\nL 373.282812 101.817532 \r\nL 373.282812 100.944279 \r\nL 373.282812 100.071026 \r\nL 373.282812 99.197773 \r\nL 373.282812 98.32452 \r\nL 373.282812 97.451267 \r\nL 373.282812 96.578014 \r\nL 373.282812 95.704761 \r\nL 373.282812 94.831508 \r\nL 373.282812 93.958255 \r\nL 373.282812 93.085002 \r\nL 373.282812 92.211749 \r\nL 373.282812 91.338496 \r\nL 373.282812 90.465243 \r\nL 373.282812 89.59199 \r\nL 373.282812 88.718737 \r\nL 373.282812 87.845484 \r\nL 373.282812 86.972231 \r\nL 373.282812 86.098978 \r\nL 373.282812 85.225725 \r\nL 373.282812 84.352472 \r\nL 373.282812 83.479219 \r\nL 373.282812 82.605966 \r\nL 373.282812 81.732713 \r\nL 373.282812 80.85946 \r\nL 373.282812 79.986207 \r\nL 373.282812 79.112954 \r\nL 373.282812 78.239701 \r\nL 373.282812 77.366448 \r\nL 373.282812 76.493195 \r\nL 373.282812 75.619942 \r\nL 373.282812 74.746689 \r\nL 373.282812 73.873436 \r\nL 373.282812 73.000183 \r\nL 373.282812 72.12693 \r\nL 373.282812 71.253677 \r\nL 373.282812 70.380424 \r\nL 373.282812 69.507171 \r\nL 373.282812 68.633918 \r\nL 373.282812 67.760665 \r\nL 373.282812 66.887412 \r\nL 373.282812 66.014159 \r\nL 373.282812 65.140905 \r\nL 373.282812 64.267652 \r\nL 373.282812 63.394399 \r\nL 373.282812 62.521146 \r\nL 373.282812 61.647893 \r\nL 373.282812 60.77464 \r\nL 373.282812 59.901387 \r\nL 373.282812 59.028134 \r\nL 373.282812 58.154881 \r\nL 373.282812 57.281628 \r\nL 373.282812 56.408375 \r\nL 373.282812 55.535122 \r\nL 373.282812 54.661869 \r\nL 373.282812 53.788616 \r\nL 373.282812 52.915363 \r\nL 373.282812 52.04211 \r\nL 373.282812 51.168857 \r\nL 373.282812 50.295604 \r\nL 373.282812 49.422351 \r\nL 373.282812 48.549098 \r\nL 373.282812 47.675845 \r\nL 373.282812 46.802592 \r\nL 373.282812 45.929339 \r\nL 373.282812 45.056086 \r\nL 373.282812 44.182833 \r\nL 373.282812 43.30958 \r\nL 373.282812 42.436327 \r\nL 373.282812 41.563074 \r\nL 373.282812 40.689821 \r\nL 373.282812 39.816568 \r\nL 373.282812 38.943315 \r\nL 373.282812 38.070062 \r\nL 373.282812 37.196809 \r\nL 373.282812 36.323556 \r\nL 373.282812 35.450303 \r\nL 373.282812 34.57705 \r\nL 373.282812 33.703797 \r\nL 373.282812 32.830544 \r\nL 373.282812 31.957291 \r\nL 373.282812 31.084038 \r\nL 373.282812 30.210785 \r\nL 373.282812 29.337532 \r\nL 373.282812 28.464279 \r\nL 373.282812 27.591026 \r\nL 373.282812 26.717773 \r\nL 373.282812 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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_decision_boundary(poly_svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## 使用多项式核函数的SVM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "def PolynomialKernelSVC(degree, C=1.0):\n",
    "    return Pipeline([\n",
    "        (\"std_scaler\", StandardScaler()),\n",
    "        (\"kernelSVC\", SVC(kernel=\"poly\", degree=degree, C=C))\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('std_scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('kernelSVC',\n",
       "                 SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3,\n",
       "                     gamma='auto_deprecated', kernel='poly', max_iter=-1,\n",
       "                     probability=False, random_state=None, shrinking=True,\n",
       "                     tol=0.001, verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ],
   "source": [
    "poly_kernel_svc = PolynomialKernelSVC(degree=3)\n",
    "poly_kernel_svc.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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203.988134 \r\nL 373.282812 203.114881 \r\nL 373.282812 202.241628 \r\nL 373.282812 201.368375 \r\nL 373.282812 200.495122 \r\nL 373.282812 199.621869 \r\nL 373.282812 198.748616 \r\nL 373.282812 197.875363 \r\nL 373.282812 197.00211 \r\nL 373.282812 196.128857 \r\nL 373.282812 195.255604 \r\nL 373.282812 194.382351 \r\nL 373.282812 193.509098 \r\nL 373.282812 192.635845 \r\nL 373.282812 191.762592 \r\nL 373.282812 190.889339 \r\nL 373.282812 190.016086 \r\nL 373.282812 189.142833 \r\nL 373.282812 188.26958 \r\nL 373.282812 187.396327 \r\nL 373.282812 186.523074 \r\nL 373.282812 185.649821 \r\nL 373.282812 184.776568 \r\nL 373.282812 183.903315 \r\nL 373.282812 183.030062 \r\nL 373.282812 182.156809 \r\nL 373.282812 181.283556 \r\nL 373.282812 180.410303 \r\nL 373.282812 179.53705 \r\nL 373.282812 178.663797 \r\nL 373.282812 177.790544 \r\nL 373.282812 176.917291 \r\nL 373.282812 176.044038 \r\nL 373.282812 175.170785 \r\nL 373.282812 174.297532 \r\nL 373.282812 173.424279 \r\nL 373.282812 172.551026 \r\nL 373.282812 171.677773 \r\nL 373.282812 170.80452 \r\nL 373.282812 169.931267 \r\nL 373.282812 169.058014 \r\nL 373.282812 168.184761 \r\nL 373.282812 167.311508 \r\nL 373.282812 166.438255 \r\nL 373.282812 165.565002 \r\nL 373.282812 164.691749 \r\nL 373.282812 163.818496 \r\nL 373.282812 162.945243 \r\nL 373.282812 162.07199 \r\nL 373.282812 161.198737 \r\nL 373.282812 160.325484 \r\nL 373.282812 159.452231 \r\nL 373.282812 158.578978 \r\nL 373.282812 157.705725 \r\nL 373.282812 156.832472 \r\nL 373.282812 155.959219 \r\nL 373.282812 155.085966 \r\nL 373.282812 154.212713 \r\nL 373.282812 153.33946 \r\nL 373.282812 152.466207 \r\nL 373.282812 151.592954 \r\nL 373.282812 150.719701 \r\nL 373.282812 149.846448 \r\nL 373.282812 148.973195 \r\nL 373.282812 148.099942 \r\nL 373.282812 147.226689 \r\nL 373.282812 146.353436 \r\nL 373.282812 145.480183 \r\nL 373.282812 144.60693 \r\nL 373.282812 143.733677 \r\nL 373.282812 142.860424 \r\nL 373.282812 141.987171 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot_decision_boundary(poly_kernel_svc, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0,0], X[y==0,1])\n",
    "plt.scatter(X[y==1,0], X[y==1,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}